The OncoArray chip is a custom chip manufactured by Illumina that was designed to provide both a basic GWAS backbone and to provide discovery, replication, and fine mapping of SNPs for cancers of the breast, colon, lung, ovary, and prostate. More than 4000 Sister Study and 3000 Two Sister subjects genotyped at CIDR using the OncoArray. DNA quality for our samples was good, and fewer than 1% of women were excluded because of low call rate or other QC measures. Among Sister Study women, genotypes were generated for approximately 1700 women who developed invasive cancer, 500 with ductal carcinoma in situ (DCIS), and 67 with lobular carcinoma in situ (LCIS); along with more than 1900 women from a random sample of Sister Study cohort. Approximate 85% of women were non-Hispanic White, with non-Hispanic Black women representing the largest minority group of 8%. In addition, genotype data were generated on about 150 Sister Study subjects who developed ovarian cancer, including 18 women who had both breast and ovarian cancers. About 1,200 women with early onset (before age 50) invasive breast cancer and family members from the Two Sister study were also genotyped using saliva sample DNA. Our genotype and phenotype data were contributed to both breast cancer (BCAC/DRIVE/CIMBA) and ovarian cancer (OCAC) OncoArray consortia analyses Using a family-based GWAS approach we looked for direct evidence of mitochondrial, X-linked, and maternally-mediated genetic effects in 1,279 young-onset breast cancer cases in the Two Sister Study, but found no evidence of significant effects OBrien et al, Eur J Hum Genet 201653. We also explicitly examined the relationship between a set of 77 established breast cancer SNPs and the risk of young onset disease in the Two Sister study. Polygenic risk scores suggest the overall joint effect of these SNPs was more than additive and consistent with a multiplicative effect; for 59% of unaffected-affected sib pairs, the genetic risk scores were higher in the affected sib Shi et al, 2017 Breast Cancer Res Treat Bladder cancer is the 4th most common cancer in American men and is strongly associated with both smoking and chemical exposures. Past GWAS studies of bladder cancer have identified 15 genomic regions that show association with risk, but there are several additional areas that appear promising but had not reached statistical significance. As part of a large replication effort to examine these regions in more detail, my laboratory genotyped cases and controls for a set of 10 candidate SNPs using DNA from my NC Bladder Cancer Study. Two regions were statistically significant, one in a gene desert at 20p12.2 and one within the MCF2L gene at 13q34. Although the functional significance of the SNPs remains to be determined, in case-case analysis the 20p12.2 SNP shows stronger association with more invasive disease. Figueroa et al, Hum Mol Genet 201661 Environmental exposures cause DNA alterations that may increase cancer risk. Examples in which specific agents cause specific changes in tumor tissue include UV light-induced C to T transitions at dipyrimidine sites in skin cancer, G to T mutations in smoking-associated lung cancer, and p53 codon 249 mutations in aflatoxin-associated hepatocellular carcinoma. Next-Generation sequencing has opened the door to mutational analysis of tumors, but the number and pattern of acquired somatic mutations in normal cells is unknown. In a collaboration with Dr. Dmitry Gordenin at NIEHS I helped design a study to measure mutations from single normal cells, providing one of the first estimates of mutational load in normal cells. Using the NIEHS Clinical Research Unit we obtained skin biopsies from areas with and without sun exposure. Individual cells would be expected to acquire different sets of mutations, so in order to have sufficient DNA from individual cells, we clonally expanded single cell cultures and independently sequenced them. We find that each skin cell has at least one chromosomal re-arrangement and between 600 and 13,000 base substitutions, with sun-exposed cells having more than 2 fold higher mutational load than unexposed cells. As DNA technologies continue to improve, it will be possible to use population-based studies to link less well-characterized environmental exposures to DNA mutations and disease riskproviding an exciting new tool for understanding cancer risk. Saini et al, 2016 PLoS Genet